A methodology for supercapacitor capacitance prediction using machine learning algorithms

Research on the use of new materials for super capacitors has been carried out due to the growth in global energy demand, the need to use renewable energy more efficiently, reduce carbon emissions, and specific applications that require large energy storage in conjunction with rapid charging and discharging. Due to the high cost, time spent, and professionals trained to study new materials, a great alternative or auxiliary tool is the use of machine learning algorithms to predict the capacitance of new materials in super capacitors. This article aims to present a methodology providing extremely useful references and comments for the construction and evaluation of super capacitor capacitance prediction models.